Evaluating the importance of individual parameters in structural equation modeling: the need for type I error control

The use of structural equation modeling in personality research has been increasing steadily over the past few decades. In evaluating the adequacy of a particular model researchers are often interested in evaluating not only the overall fit of the model, but also which of the proposed parameters are significant. Researchers who apply unrestricted post hoc model modifications, or who evaluate the significance of individual parameters without adopting some form of type I error control, risk capitalizing on chance. A Monte Carlo study was used to demonstrate the effectiveness of simple Bonferroni-type procedures for controlling the rate of type I errors when multiple parameters are evaluated in the structural portion of a theoretical model.